Large volumes of image data are available in many fields of use. Such large image data volumes must be analyzed in accordance with predetermined criteria. For example, in the area of military reconnaissance, it is frequently the case that large quantities of image data of scenes or terrains are acquired by sensors. These acquired image data must be scrutinized with regard to the presence of installations, vehicles, infrastructure features and so forth in the terrain. These image data are generally acquired in large numbers which must be processed and evaluated within given time limitations. The objects to be recognized may have any random dimensions and may have a structure that characterizes any particular object. The structure of the object may be rather complex or it may be simple. In all these cases it is desirable to perform an automatic image analysis as rapidly as possible.
Other fields of application of this type of image evaluation are, for example, to be found in the area of medical diagnosis, for example when it is necessary to examine a large number of X-ray images, for instance for recognizing anomalies such as tumors or the like. Another example where an automatic image analysis method is employed is in the area of police work. This area includes the search for missing persons, the monitoring of border crossings or the like. In all these areas a reliable automatic rapid image analysis method provides great advantages.
General, theoretical approaches for such analysis method for the recognition of objects in images are known from an article in “Technical Report ISIS TR-4” by T. Dodd, University of South Hampton, 1996. This article describes different possible approaches to the analysis of digital images for the purpose of recognizing objects in such images.
Individual steps for analyzing images are known from the following publications. Different methods for a rough classification of objects are described in an article “Classifier and Shift-Invariant Automatic Target Recognition Neural Networks”, by D. P. Casasent, L. M. Neiberg published in “Neural Networks”, Vol. 8, No. 7/8, by Elsevier Science Ltd., 1995. General methods for the dissecting or decomposing of a digital image into image components represented by signals are found, for example in a publication “Practice of Digital Image Processing and Pattern Recognition” by P. Haberaecker, published by Carl Hanser Verlag, 1995. The so-called “Ensemble Theory for Classifiers” has been described in an article “Machine Learning Research” by T. G. Dietterich that appeared in “Al Magazine”, Vol. 18, No. 4, 1997, published by AAAI Press. A possible way of merging or fusing individual results of an analysis are described in “Vehicle Detection in Infrared Line Scan Imagery Using Belief Networks” by P. G. Dubksbury, D. M. Booth and C. J. Radford, published at the 5th International Conference of Image Processing and Application, Edinburgh, 1995.
German Patent Publication DE 44 38 235 A1 discloses a method for recognizing objects in natural surroundings. The known method uses several classifiers which operate in accordance with a predetermined, simple rule. A disadvantage of the known method is seen in that it can function only, and on principle, under the assumptions that the objects to be recognized are compact and at least partially symmetric. Thus, the method is useful only for the recognition of point-shaped objects. The method cannot be used for recognizing larger and structurized objects.
German Patent Publication DE 196 39 884 C2 discloses a system for recognizing patterns in images. For classifying an object, the known system requires the input and processing of feature or characterizing parameters of the object in order to produce a recognition result based on such parameters. The system requires the use of a complex classifier. A disadvantage of the known system is seen in that only a clearly determined object can be recognized.